$GTCH The open research is focused on the efforts

$GTCH The open research is focused on the efforts combining Kirlian imaging with the use of machine learning technology to possibly detect early disease symptoms. GBT/Tokenize is conducting experiments with a few advanced algorithms, one of them is a private derivative of the Genetic algorithm. The genetic algorithm is a method for solving both constrained and unconstrained optimization problems that are based on natural selection, the process that drives biological evolution. The algorithm is planned to find pattern similarities in Kirlian images in living tissues in order to categorize potential health related issues. Kirlian imaging produces typical features such as graphical protuberances, halos, and discharge patterns, which can be analyzed by an AI computer program as unique patterns, and categorized as possible criteria for early symptoms identification. GBT/Tokenize is conducting experiments with advanced algorithms and methods to analyze energy fields generated by living organs at set periods. These image's auras will be graphically analyzed to determine patterns that are associated with possible health related symptoms. GBT/Tokenize plans to develop neural network based pattern recognition technology to detect-and-associate unique patterns with related, possible health issues. The research is planned to be conducted over a period of one year and based on its results the company will evaluate the feasibility of implementation of such techniques within its qTerm human vitals product in order to provide further health information for the user's benefit.

https://www.nasdaq.com/press-release/gbt-toke...2020-12-01

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